Recommendation system is an information filtering technique, which provides users with information, which he/she may be interested in. It helps in addressing the information overload problem by retrieving the information desired by the user based on his or similar users' preferences and interests. Service recommender systems proves to be a valuable tool for providing appropriate recommendations to users. Most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users preferences, and therefore fails to meet users personalized requirements. A Keyword-Aware Service Recommendation method, named KASR has been prposed to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environment, KASR is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm.

Keywords- Recommendation System, Hadoop, MapReduce, Filtering Algorithms

 Software product line represents systems having conceptual similarity. All the systems in the product line have commonalities and variability. Feature models are often used to represent this intrinsic commonality and variability. They have a tree-like structure. A feature model which is low in quality will have negative effect over all the products belonging to the product line. Thus, early indicators in the form of metrics are required to assess quality of feature models. Assessments of quality attributes will help in avoiding the consequences of inferior quality and faulty design at the later stages of production. Quality attributes are of two types: first type of attributes is internal which can be measured through product related features like length, complexity, efficiency etc. Second are external attributes which can be assessed once the product is fully functional like usability, reliability, maintainability etc. Usability is an external attribute which focuses on easy and efficient product usage. In reference to feature models usability is an essential quality to be possessed. In this paper, we intend to validate an existing structural metrics for software product line feature models. In our research, we try to examine whether the available metrics are fair indicators of the three main sub characteristics of usability viz. learnability, understandability and communicativeness. We try to analysis whether these existing metrics for feature models have a correlation with feature model usability. For this we have employed statistical correlation techniques. Results obtained from the empirical validation shows that the metrics are correlated to the subjective perception regarding the usability of the feature models.
Keywords-Software product lines, feature models, structural complexity metrics, empirical validation.

Railways comprise a large infrastructure and are an important mode of transportation in many countries. Railways are the lifelines of a country. The railways have become a new means of transportation owing to their capacity, speed, and reliability, being closely associated with passenger and goods transportation; they have high risk associated with them in terms of human lives and cost of assets . The poor maintenance of the railways can lead to accidents. New technologies for railways and better safety measures are introduced time to time but still accidents do occur. Thus, a proper strategy is required for maintenance and inspection of tracks.
      Detection and maintenance of rail defects are major issues for the rail community all around the world. The defects mainly include weld problems, internal defects worn out rails, head checks, squats, palling and shelling, corrugations and rolling contact fatigue (RCF) initiated problems such as surface cracks. If these defects are not handled and corrected they can lead to rail breaks and accidents. There are numerous challenges to rail community and the infrastructure maintenance people such as to perform effective inspection and cost effective maintenance decisions. If these issues are taken care of properly, inspection and maintenance decisions can reduce potential risk of rail breaks and derailment.                     
      The detection of cracks in rails is a challenging problem, and much research effort has been spent in the development of reliable, repeatable crack detection methods for use on in-service rails. While crack detection in the rail head and shear web is reliably achieved using ultrasonic and eddy current methods, neither technique is particularly effective for the detection of cracks in the rail foot. The authors present a new crack detection method for rail, which utilizes the change in infrared emission of the rail surface during the passage of a train wheel. Initial data from this infrared method are presented, from studies of both a laboratory-based three-point bend specimen and a short section of rail. The results of these two studies confirm the ability of the proposed method to locate and quantify surface-connected notches and cracks.

General Terms-Rail Crack Detector System, Railway Safety System, Wireless Sensor Network, Monitoring System etc.

Keywords-crack detection, rail track inspection, sensor node etc

Cloud computing has been one of the most prominent buzzwords of the IT industry due to its revolutionary model of computing as utility. Cloud computing promises increased flexibility scalability and reliability with promising decreased operational and maintenance costs as every organization is accumulating tons and tons of data, it is quite difficult for the organizations to manage that huge amount of data, So instead of self-maintaining the data the organizations can outsource their data to the cloud storage and can reduce the data management overhead. Cloud storage offers an abstraction of infinite storage space for clients to outsource their data and access it in a pay-as-you-use manner. While we are outsourcing the data to the cloud storage services, managed by third parties, several security concerns will arise in terms of privacy and integrity of the data .The traditional and cloud storage are becoming highly reliable in recovering the data from a disaster/ failure. For them to be reliable the cloud service providers are creating multiple redundant copies of the data and they were spread through the cloud for reliability and availability, without the knowledge of the data owner. One specific issue is as that many number of copies of the data is created, it is hard to delete all those copies, the cloud service may forget to remove all the copies / intentionally may not delete. Some of the copies of data upon request of deleting the data by the data owner. So to ensure the privacy and integrity of data outsourced to the cloud storage, we must design a practically implementable and readily deployable application which ensures security of the outsourced data and provides the data owner the assurance that the data was in a safe state, and all the copies of the data were deleted .this paper discusses several mechanisms available to ensure the assured file deletion. First we discuss the advantages and disadvantages of various mechanisms, and then we propose a practically implementable, readily deployable mechanism which works on top of the existing cloud architecture with minimal management overhead.

Key words: cloud storage, policy based file assured deletion, time based deletion